Accuracy Of Fault Prediction Research Articles

LIMCR: Less-Informative Majorities Cleaning Rule Based on Naïve Bayes for Imbalance Learning in Software Defect Prediction

Software defect prediction (SDP) is an effective technique to lower software module testing costs. However, the imbalanced distribution almost exists in all SDP datasets and restricts the accuracy of defect prediction. In order to balance the data distribution reasonably, we propose a novel resampling method LIMCR on the basis of Naïve Bayes to optimize and improve the SDP performance. The main idea of LIMCR is to remove less-informative majorities for rebalancing the data distribution after evaluating the degree of being informative for every sample from the majority class. We employ 29 SDP datasets from the PROMISE and NASA dataset and divide them into two parts, the small sample size (the amount of data is smaller than 1100) and the large sample size (larger than 1100). Then we conduct experiments by comparing the matching of classifiers and imbalance learning methods on small datasets and large datasets, respectively. The results show the effectiveness of LIMCR, and LIMCR+GNB performs better than other methods on small datasets while not brilliant on large datasets.

Machine Learning-Based Approach for Hardware Faults Prediction

Hardware failures are undesired but a common problem in circuits. Such failures are inherently due to the aging of circuitry or variation in circumstances. In critical systems, customers demand the system never to fail. Several self-healing and fault tolerance techniques have been proposed in the literature for recovering a circuitry from a fault. Such techniques come to the rescue when a fault has already occurred but they are typically uninformed about the possibility of an impending failure (i.e., fault prediction), which can be used as a pre-stage to fault tolerance and self-healing. This paper presents an approach to early fault prediction of circuits. The proposed method uses Fast Fourier Transform (FFT) to get the fault frequency signature, Principal Component Analysis (PCA) to get the most important data with reduced dimension, and Convolutional Neural Network (CNN) to learn and classify the fault. The proposed method is validated for working in different circuits by testing it using two circuits: comparator and amplifier. The comparator and amplifier are implemented using 45 nm technology on HSPICE to extract the failures dataset in terms of voltage, current, temperature, noise, and delay. This extracted data is used for training the proposed approach using Tensorflow. To the best of our knowledge, this is the first work of fault prediction at the transistor level for hardware system. The proposed approach considers aging, short-circuit, and open-circuit faults, and it provides a fault prediction accuracy of 98.93% and 98.91% for comparator and amplifier circuits, respectively. The proposed method is tested for two different circuits for its validation, and it consumes 1.08 W for Altera Arria 10 GX FPGA device.

Intelligent fault prediction of rolling bearing based on gate recurrent unit and hybrid autoencoder

Accurate fault prediction of rolling bearing can predict the operation condition in advance, which is an important means to ensure the safety and reliability of rotating machinery. Aimed at the data processing of rolling bearing vibration signal with multi-fault and long time series, an intelligent fault prediction model based on gate recurrent unit and hybrid autoencoder is proposed in this paper. Firstly, vibration signals of multi-faults are brought into multi-layer gate recurrent unit model for multi-step and multi-variable time series prediction. Secondly, variational autoencoder is used for data augmentation of fault samples. Thirdly, the augmented fault samples are brought into stacked denoising autoencoder for noise reduction and fault prediction. Finally, fault prediction results of rolling bearing can be achieved on the basis of gate recurrent unit and hybrid autoencoder of variational autoencoder and stacked denoising autoencoder. The bearing datasets of Case Western Reserve University are used to verify the effectiveness of the proposed method. Comparative experiment results show that the proposed fault prediction model has more accurate time series prediction result and higher fault prediction accuracy than other deep learning models. With 98.68% accuracy of fault prediction, the proposed fault prediction model can be taken as an effective tool for intelligent predictive maintenance of rolling bearing.

Research on State Recognition and Failure Prediction of Axial Piston Pump Based on Performance Degradation Data

Degradation state recognition and failure prediction are the key steps of prognostic and health management (PHM), which directly affect the reliability of the equipment and the selection of preventive maintenance strategy. Given the problem that the distinction between feature vectors is not obvious and the accuracy of fault prediction is low, a method based on multi-class Gaussian process classification and Gaussian process regression (GPR) is studied by the vibration signal and flow signal in six degraded states of the axial piston pump. For degradation state recognition, the variational mode decomposition (VMD) was used to decompose the vibration signal, and obtaining intrinsic mode function (IMF) components with rich information. Subsequently, multi-scale permutation entropy (MPE) was employed to select feature vectors of IMF components in different states. In order to reduce feature dimensions and improve recognition performance, ReliefF was used to select feature vectors with high weight, then a method based on multi-class Gaussian process classification was established by using these feature vectors to realize the research on the degradation state recognition. The test results demonstrate that the method can effectively identify the degradation state. Its recognition rate reaches 98.9%. Besides, for failure prediction, through the analysis of the wear process and wear mechanism of the valve plate, the curve fitting between the flow and the wear amount was performed by GPR to realize the failure prediction of the axial piston pump. Depending on the evaluation index, the GPR obtained a better failure prediction effect. The results will assist in the realization of predictive maintenance, and which also has significant practical value in project items.

Proposed approach to predict software faults detection using Entropy

The major challenge is to validate software failure dataset by finding unknown model parameters used. For software assurance, previously many attempts were made based using classical classifiers as Decision Tree, Naive Bayes, and k-NN for software fault prediction. But the accuracy of fault prediction is very low as defect prone modules are very small as compared to defect-free modules. So, for solving modules fault classification problems and enhancing reliability accuracy, a hybrid algorithm proposed on particle swarm optimization and modified genetic algorithm for feature selection and bagging for effective classification of defective or non-defective modules in a dataset. This paper presents an empirical study on NASA metric data program datasets, using the proposed hybrid algorithm and results showed that our proposed hybrid approach enhances the classification accuracy compared with existing methods.

Proposed software faults detection using hybrid approach

AbstractThe major challenge is to validate software failure dataset by finding unknown model parameters used. For software assurance, previously many attempts were made based using classical classifiers as decision tree, Naïve Bayes, and k‐nearest neighbor for software fault prediction. But the accuracy of fault prediction is very low as defect prone modules are very small as compared to defect‐free modules. So, for solving modules fault classification problems and enhancing reliability accuracy, a hybrid algorithm proposed on particle swarm optimization and modified genetic algorithm for feature selection and bagging for effective classification of defective or nondefective modules in a dataset. This paper presents an empirical study on National Aeronautics and Space Administration Metric Data Program datasets, using the proposed hybrid algorithm and results showed that our proposed hybrid approach enhances the classification accuracy compared with existing methods.

Remote machine mode detection in cold forging using vibration signal

Detecting machine mode can allow smarter process monitoring systems and more accurate fault prediction without external information. A remote machine monitoring system was installed on a cold heading machine in the factory of an automotive fastener manufacturing company. The process monitoring system was non-intrusive and was designed to measure vibration. The end goal of the study was to predict tool wear, but part classification was required first, as the machine produced multiple parts which produced different vibration signals. The collected vibration data was processed using wavelet transform and passed through a convolutional neural network for part classification. This method achieved part classification accuracy as high as 86% when looking at data for a 1-month period. The results show that meaningful classification features are present in the data using the process monitoring system as designed.

Fuzzy algorithm-based fault analysis for automated production lines

In this paper, an in-depth analysis of automated production line faults based on fuzzy algorithms is carried out and based on an in-depth investigation of the mechanism of equipment faults, research work on equipment state prediction and production line fault diagnosis is carried out, and the corresponding algorithm model workflow is given, which has some practical application value for improving the accuracy of production line fault prediction. The algorithm with data mining association rules is proposed to extract the confidence parameters of the conditional state fuzzy net model, and an inverse conditional state fuzzy net is established based on the conditional state fuzzy net for fault diagnosis and reasoning, and a dynamic confidence level reasoning mechanism is also established for reverse reasoning based on the iterative algorithm of maximum algebra. To monitor the operating status of the production line more intuitively, a production line fault prediction and analysis system is developed based on the platform, which mainly includes a data management module, state monitoring module, state prediction module, fault diagnosis module, and maintenance advice module, which can more easily realize the monitoring of the production line equipment state and fault early warning prompting, making the system more practical value.

Research on Mechanical Fault Prediction Method Based on Multifeature Fusion of Vibration Sensing Data.

Vibration sensing data is an important resource for mechanical fault prediction, which is widely used in the industrial sector. Artificial neural networks (ANNs) are important tools for classifying vibration sensing data. However, their basic structures and hyperparameters must be manually adjusted, which results in the prediction accuracy easily falling into the local optimum. For data with high levels of uncertainty, it is difficult for an ANN to obtain correct prediction results. Therefore, we propose a multifeature fusion model based on Dempster-Shafer evidence theory combined with a particle swarm optimization algorithm and artificial neural network (PSO-ANN). The model first used the particle swarm optimization algorithm to optimize the structure and hyperparameters of the ANN, thereby improving its prediction accuracy. Then, the prediction error data of the multifeature fusion using a PSO-ANN is repredicted using multiple PSO-ANNs with different single feature training to obtain new prediction results. Finally, the Dempster-Shafer evidence theory was applied to the decision-level fusion of the new prediction results preprocessed with prediction accuracy and belief entropy, thus improving the model’s ability to process uncertain data. The experimental results indicated that compared to the K-nearest neighbor method, support vector machine, and long short-term memory neural networks, the proposed model can effectively improve the accuracy of fault prediction.

Evaluation of CART, CHAID, and QUEST algorithms: a case study of construction defects in Taiwan

ABSTRACTThe type and number of defects constitute a major indicator of project quality and are thus emphasized in project management. Therefore, it is necessary to explore appropriate tools and methods to train/test/analyze defect-related big data in order to effectively explain the cause/rule/importance of the defect, to understand the focus of site management, and to effectively prevent defects. The aim of this study is to explore the capability of three kinds of decision tree algorithms, namely classification and regression tree (CART), chi-squared automatic interaction detection (CHAID) and quick unbiased efficient statistical tree algorithms (QUEST), in predicting the construction project grade given defects. Firstly, a total of 499 types of defects were identified after the analysis of the data of 990 projects obtained from the Public Construction Management Information System (PCMIS). Secondly, inspection scores and defect frequencies were estimated to perform cluster analysis for re-grouping the data to create project grade. Thirdly, decision trees were used to classify rules for defects and project grades. The results revealed that, among the three algorithms, CHAID generated the most classification rules and exhibited the highest defect prediction accuracy. The finding of this research can improve the defect prediction accuracy and management effectiveness for construction industry.

A novel modified undersampling (MUS) technique for software defect prediction

AbstractBackground and aim: Many sophisticated data mining and machine learning algorithms have been used for software defect prediction (SDP) to enhance the quality of software. However, real‐world SDP data sets suffer from class imbalance, which leads to a biased classifier and reduces the performance of existing classification algorithms resulting in an inaccurate classification and prediction. This work aims to improve the class imbalance nature of data sets to increase the accuracy of defect prediction and decrease the processing time. Methodology: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time. It consists of a modified undersampling method and a correlation feature selection (CFS) method. Results: The results from ten open source project data sets showed that the proposed model improves the accuracy in terms of F1‐score to 0.52 ∼ 0.96, and hence it is proximity reached best F1‐score value in 0.96 near to 1 then it is given a perfect performance in the prediction process. Conclusion: The proposed model focuses on balancing the class of data sets to increase the accuracy of prediction and decrease processing time using the proposed model.

Software Defect Prediction via Attention-Based Recurrent Neural Network

In order to improve software reliability, software defect prediction is applied to the process of software maintenance to identify potential bugs. Traditional methods of software defect prediction mainly focus on designing static code metrics, which are input into machine learning classifiers to predict defect probabilities of the code. However, the characteristics of these artificial metrics do not contain the syntactic structures and semantic information of programs. Such information is more significant than manual metrics and can provide a more accurate predictive model. In this paper, we propose a framework called defect prediction via attention-based recurrent neural network (DP-ARNN). More specifically, DP-ARNN first parses abstract syntax trees (ASTs) of programs and extracts them as vectors. Then it encodes vectors which are used as inputs of DP-ARNN by dictionary mapping and word embedding. After that, it can automatically learn syntactic and semantic features. Furthermore, it employs the attention mechanism to further generate significant features for accurate defect prediction. To validate our method, we choose seven open-source Java projects in Apache, using F1-measure and area under the curve (AUC) as evaluation criteria. The experimental results show that, in average, DP-ARNN improves the F1-measure by 14% and AUC by 7% compared with the state-of-the-art methods, respectively.

An Approach for the Prediction of Number of Software Faults Based on the Dynamic Selection of Learning Techniques

Determining the most appropriate learning technique(s) is vital for the accurate and effective software fault prediction (SFP). Earlier techniques used for SFP have reported varying performance for different software projects and none of them has always reported the best performance across different projects. The problem of varying performance can be solved by using an approach, which partitions the fault dataset into different module subsets, trains learning techniques for each subset, and integrates the outcomes of all the learning techniques. This paper presents an approach that dynamically selects learning techniques to predict the number of software faults. For a given testing module, the presented approach first locates its neighbor module subset that contained modules similar to testing module using a distance function and then chooses the best learning technique in the region of that module subset to make the prediction for testing module. The learning technique is selected based on its past performance in the region of module subset. We have performed an evaluation of the proposed approach using fault datasets garnered from the PROMISE data repository and Eclipse bug data repository. Experimental results showed that the proposed approach led to an improved performance when predicting the number of faults in software systems.

Effort-aware and just-in-time defect prediction with neural network.

Effort-aware just-in-time (JIT) defect prediction is to rank source code changes based on the likelihood of detects as well as the effort to inspect such changes. Accurate defect prediction algorithms help to find more defects with limited effort. To improve the accuracy of defect prediction, in this paper, we propose a deep learning based approach for effort-aware just-in-time defect prediction. The key idea of the proposed approach is that neural network and deep learning could be exploited to select useful features for defect prediction because they have been proved excellent at selecting useful features for classification and regression. First, we preprocess ten numerical metrics of code changes, and then feed them to a neural network whose output indicates how likely the code change under test contains bugs. Second, we compute the benefit cost ratio for each code change by dividing the likelihood by its size. Finally, we rank code changes according to their benefit cost ratio. Evaluation results on a well-known data set suggest that the proposed approach outperforms the state-of-the-art approaches on each of the subject projects. It improves the average recall and popt by 15.6% and 8.1%, respectively.

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing.

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

Degradation Trend Prediction for Rotating Machinery Using Long-Range Dependence and Particle Filter Approach

Timely maintenance and accurate fault prediction of rotating machinery are essential for ensuring system availability, minimizing downtime, and contributing to sustainable production. This paper proposes a novel approach based on long-range dependence (LRD) and particle filter (PF) for degradation trend prediction of rotating machinery, taking the rolling bearing as an example. In this work, the degradation prediction is evaluated based on two health indicators time series; i.e., equivalent vibration severity (EVI) time series and kurtosis time series. Specifically, the degradation trend prediction issues here addressed have the following two distinctive features: (i) EVI time series with weak LRD property and (ii) kurtosis time series with sharp transition points (STPs) in the forecasted region. The core idea is that the parameters distribution of the LRD model can be updated recursively by the particle filter algorithm; i.e., the parameters degradation of the LRD model are restrained, and thus the prognostic results could be generated real-time, wherein the initial LRD model is designed randomly. The prediction results demonstrate that the significant improvements in prediction accuracy are obtained with the proposed method compared to some state-of-the-art approaches such as the autoregressive–moving-average (ARMA) model and the fractional order characteristic (FOC) model, etc.

Software Fault Prediction Using Filtering Feature Selection in Cluster-Based Classification

The high accuracy of software fault prediction can help testing effort and improving software quality. Previous researchers had proposed the combination of Entropy-Based Discretization (EBD) and Cluster-Based Classification (CBC). However, the irrelevant and redundant features in software fault dataset tend to decrease the prediction accuracy value. This study proposes improvement of CBC outcomes by integrating filtering feature selection methods. Filtering feature selection methods that will be integrated with CBC i.e. Information Gain (IG), Gain Ratio (GR), and One-R (OR). Based on the research using 2 datasets NASA public MDP (i.e. PC2 and PC3), the result shows that the combination of CBC and IG yields the best average accuracy value compared to GR and OR. It generates 67.52% average of probability detection (pd) and 37.42% average of probability false alarm (pf). While CBC without feature selection yields 65.38% average pd and 49.95% average pf. It can be concluded that IG can improve CBC outcomes by increasing 2.14% average pd and reducing 12.53% average pf

A Nonlinear Manifold Detection based Model for Software Defect Prediction

Software defect prediction requires developing a new technique which aims at accurately predict defective modules in software system with minimum time and space complexity as well as lesser computational cost. As such, a new model based on Nonlinear Manifold Detection Techniques has been proposed to eliminate undesirable and irrelevant attributes of high dimensional datasets by dimension reduction with more prediction accuracy and improved software quality. In this paper, a novel step towards achieving the goal by developing a new model based on Nonlinear MDTs and comparing its effectiveness with existing Feature Selection techniques for identifying the most accurate defect prediction method. The performance of different classification methods with both new and existing techniques has been evaluated, compared and also tested statistically by using Friedman test and Post Hoc analysis. The result proved that new proposed model based on Nonlinear MDTs is better performance oriented compared to accuracy level of all other techniques.

An improved twin support vector machine based on multi-objective cuckoo search for software defect prediction

Recently, software defect prediction (SDP) has drawn much attention as software size becomes larger and consumers hold higher reliability expectations. The premise of SDP is to guide the detection of software bugs and to conserve computational resources. However, in prior research, data imbalances among software defect modules were largely ignored to focus instead on how to improve defect prediction accuracy. In this paper, a novel SDP model based on twin support vector machines (TSVM) and a multi-objective cuckoo search (MOCS) is proposed, called MOCSTSVM. We set the probability of detection and the probability of false alarm as the SDP objectives. We use TSVM to predict defected modules and employ MOCS to optimise TSVM for this dual-objective optimisation problem. To test our approach, we conduct a series of experiments on a public dataset from the PROMISE repository. The experimental results demonstrate that our approach achieves good performance compared with other SDP models.

An improved twin support vector machine based on multi-objective cuckoo search for software defect prediction

Recently, software defect prediction (SDP) has drawn much attention as software size becomes larger and consumers hold higher reliability expectations. The premise of SDP is to guide the detection of software bugs and to conserve computational resources. However, in prior research, data imbalances among software defect modules were largely ignored to focus instead on how to improve defect prediction accuracy. In this paper, a novel SDP model based on twin support vector machines (TSVM) and a multi-objective cuckoo search (MOCS) is proposed, called MOCSTSVM. We set the probability of detection and the probability of false alarm as the SDP objectives. We use TSVM to predict defected modules and employ MOCS to optimise TSVM for this dual-objective optimisation problem. To test our approach, we conduct a series of experiments on a public dataset from the PROMISE repository. The experimental results demonstrate that our approach achieves good performance compared with other SDP models.